Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation

• URL: http://arxiv.org/abs/2409.11695v1
• Date: Wed, 18 Sep 2024 04:31:22 GMT
• Title: Basket-Enhanced Heterogenous Hypergraph for Price-Sensitive Next Basket Recommendation
• Authors: Yuening Zhou, Yulin Wang, Qian Cui, Xinyu Guan, Francisco Cisternas,
• Abstract summary: Next Basket Recommendation (NBR) is a new type of recommender system that predicts combinations of items users are likely to purchase together. Existing NBR models often overlook a crucial factor, which is price, and do not fully capture item-basket-user interactions. We propose a novel method called Basket-augmented Dynamic Heterogeneous Hypergraph (BDHH)
• Score: 15.226072390133846
• License:
• Abstract: Next Basket Recommendation (NBR) is a new type of recommender system that predicts combinations of items users are likely to purchase together. Existing NBR models often overlook a crucial factor, which is price, and do not fully capture item-basket-user interactions. To address these limitations, we propose a novel method called Basket-augmented Dynamic Heterogeneous Hypergraph (BDHH). BDHH utilizes a heterogeneous multi-relational graph to capture the intricate relationships among item features, with price as a critical factor. Moreover, our approach includes a basket-guided dynamic augmentation network that could dynamically enhances item-basket-user interactions. Experiments on real-world datasets demonstrate that BDHH significantly improves recommendation accuracy, providing a more comprehensive understanding of user behavior.

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